Abstract
The present paper attempts to find out the gap between different cost and gross value of output (GVO), dynamics of the inputs use and important indicators for gross value of output of gram crop across major producing states during 2004–2005 to 2014–2015. The results corroborate that there has been a sharp increase in GVO and total cost for all the states after 2009–2010. It was found that the rapid increase in operational cost from 2009 to 2010 was due to the introduction of farm waiver scheme by Government of India in 2008–2009. It was also evident that the compound annual growth rate is higher during 2009–2010 to 2014–2015 when compared during 2004–2005 to 2007–2008. The magnitude of profit in Madhya Pradesh and Rajasthan was impressive showing the positive trend over cost. There are no evidences that show that the high productive regions have used their inputs efficiently. It was observed from the fixed effect regression analysis that efficiency of different inputs is not same for gram crops. It was revealed that the irrigation cost and pesticides cost are not significant while total labor cost seems to have been used efficiently and significantly across the states for gram cultivation over the time period.
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Notes
The amount of waiver was initially Rs. 1000 crore in 2007–2008 and it increased to Rs. 50,000 crore in up-coming four years and executed from 2007–2008 to 2011–2012. ‘Short Term Production Loan’ means a loan given in connection with the raising of crops which is to be repaid within 18 months. It will include working capital loan, not exceeding Rs. 1 lakh, for traditional and non-traditional plantations and horticulture.
The input efficiency is a dynamic concept as it varies over time period. For example, pesticides may turn out to be an inefficient input in wheat cultivation in a particular year but it may turn out to be most efficient input in year 2 as the efficiency of the farm input is controlled by many other exogenous variables such as market yard, and price [10].
The fixed effects (FE) are used in analyzing the impact of variables that vary over time. FE explores the relationship between predictor and outcome variables within an entity. Each entity has its own individual characteristics that may or may not influence the predictor variables. When using FE we assume that something within the individual may impact or bias the predictor or outcome variables and it needs to control for this. This is the rationale behind the assumption of the correlation between entity’s error term and predictor variables. FE removes the effect of those time-invariant characteristics so it can assess the net effect of the predictors on the outcome variable. Another important assumption of the FE model is that those time-invariant characteristics are unique to the individual and should not be correlated with other individual characteristics. Each entity is different therefore the entity’s error term and the constant (which captures individual characteristics) should not be correlated with the others. If the error terms are correlated, then FE is no suitable since inferences may not be correct and you need to model that relationship (i.e., use of random-effects model), this is the main rationale for the Hausman test. After checking the Hausman test (comparison between Fixed and Random effect model), fixed effect has been used in the present study.
The normality of each independent variable has been estimated. The problems of multicollinearity, heteroscedasticity and normality of residuals have been checked. The results show that the independent variables have normal and the residuals are normally distributed.
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Mandal, A. Varying Profitability and Determinants of Gram Crop Using Cost of Cultivation Data: A Fixed Effect Approach. Agric Res 11, 557–564 (2022). https://doi.org/10.1007/s40003-021-00589-1
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DOI: https://doi.org/10.1007/s40003-021-00589-1
Keywords
- Labor cost
- Pesticides cost
- Commission for the Agricultural Cost and Prices
- Fixed effect regression model